Victoria University of Wellington
Aotearoa New Zealand
East Texas A&M University
USA
Computational humour studies focus on humour detection & generation. Incorporating humour theory into this work is important (Hempelmann, 2008)
Here we go the other direction, using computational methods to form additional tests of humour theory
“…humour involves incongruity” (Ritchie, 2004)
But…variation in what we mean or how incongruity is defined in the context of humour (Ritchie, 2009)
“All humour involves some degree of incongruity, but this incongruity is not random or arbitrary – it is systematically related to other aspects of the setting.” (Ritchie, 2009, p. 299)
https://www.gocomics.com/frazz/2005/03/28
The difference (math operation) \(\approx\) The difference (who cares?)
A pun is a textual occurrence in which a sequence of sounds must be interpreted with a formal reference to a second sequence of sounds, which may, but need not, be identical to the first sequence, for the full meaning of the text to be accessed. The perlocutionary goal or effect of the pun is to generate the perception of mirth or of the intention to do so. (Attardo, 2020, pp. 177–178)
I call my horse mayo and sometimes mayo neighs
The tomb of Karl Marx is just another communist plot
For puns to work, both meanings of the Pun & Target should be viable, but also exist in a state of incongruity.
Can we test this prediction as a function of cosine distance between vector representations of pun/target words?
Corpus of 1182 pun-target pairs (Hempelmann, 2003) from a larger set (Sobkowiak, 1991)
Imperfect, heterophonic puns (i.e., not 100% sound overlap between pun-target)
For example:
word2vec
sentence-transformers
Pairwise comparisons of semantic distance as cosine distance between pun & target words
| sentence-transformers | word2vec |
| M = 0.279 (0.109) | M = 0.143 (0.203) |M| = 0.198 (0.150) |
all the synsets for the word humour
all lemmas for 2. wit
| synset | lemmas |
|---|---|
| 1. invisible (hard to see) | invisible, unseeable |
| 2. invisible (not prominent) | inconspicuous, invisible |
| synset | lemmas |
|---|---|
| 1. invisible (hard to see) | |
| 2. invisible (not prominent) | inconspicuous, |
| synset | lemmas |
|---|---|
| 1. visible (capable of being seen) | visible, seeable |
| 2. visible (obvious) | visible |
| 3. visible (present and available) | visible |
| synset | lemmas |
|---|---|
| 1. visible (capable of being seen) | |
| 2. visible (obvious) | |
| 3. visible (present and available) |
Average WN similarity (sentence-transformers): 0.422 (0.156)
| measure | mean difference | 95%CI | t | p |
|---|---|---|---|---|
| pun-WN | 0.158 | 0.144, 0.171 | 23.325 | < .001 |
| target-WN baseline | 0.139 | 0.128, 0.151 | 23.704 | < .001 |
Our results show support for theoretical claims of incongruity theory
Specifically, semantic incongruity for puns
For puns, words must be somewhat related to be appropriate in same sentence context
Contact:
Stephen Skalicky stephen.skalicky@vuw.ac.nz
Salvatore Attardo salvatore.attardo@tamuc.edu
1st Workshop on Computational Humor (CHum 2025)